Detection of DNA Sequence Variation
There is a great need in both basic and clinical research to identify DNA sequence variations with high efficiency and accuracy. The current techniques for detection of such variation can be divided into two groups: 1) detection of known mutations or polymorphisms and 2) detection of unknown mutations or polymorphisms (also referred to as mutation scanning). A variety of effective methods have been developed for detecting known mutations and polymorphisms and include techniques such as direct DNA sequencing, allele-specific oligonucleotide hybridization, allele-specific PCR, DNA arrays, and PCR/LDR. There are a variety of techniques for detecting unknown mutations, but their sensitivity and accuracy vary greatly.
Comparison of High-Throughput Techniques to Identify Unknown Mutations in Clinical Samples
Identifying unknown mutations in clinical samples presents similar difficulties as screening for known mutations, as well as some novel complications. A mutation present in a tumor sample may represent as little as 15% of the DNA sequence for that gene due to stromal contamination. Therefore, screens for unknown mutations require high sensitivity in order to identify low abundance sequence. Since most cancer genes contain multiple exons which may be altered, even for commonly mutated genes (e.g. PTEN), most assay results of a single exon will be negative. However, by pooling samples together, the probability of finding a significant mutation in a given assay increases. In order to increase the capacity of a screen by pooling samples, the technique must have a high enough sensitivity to tolerate further mutation dilution which results from pooling. Further, for uncommon germline mutations, the ability to pool samples greatly improves the throughput of evaluating large numbers of samples in multiple exons.
Other significant complications associated with screening for unknown cancer mutations are the need to: (i) Identify either frameshift, nonsense, or missense mutations, and (ii) distinguish missense mutations from germline (i.e. silent) polymorphisms. The latter is of great significance, because it is estimated that polymorphisms exist approximately once every kb in the human genome. Wang, D. G., et al., Science, 280(5366):1077–82. (1998), Li, W. H., et al., Genetics, 129(2):513–23 (1991), Lai, E., et al., Genomics, 54(1):31–38 (1998), Nickerson, D. A., et al., Nat Genet, 19(3):233–40 (1998), Harding, R. M., et al., Am. J. Hum. Genet., 60(4):772–89 (1997), Taillon-Miller, P. et al., Genome Res., 8(7): 748–54 (1998), Halushka, M. K., et al., Nat. Genet., 22(3): 239–47 (1999), and Cargill, M., et al., Nat. Genet., 22(3):231–38 (1999). Separating out the apparently less interesting polymorphisms should significantly increase the efficiency in identifying informative mutations. Unfortunately, present methods of identifying additional low frequency mutations with clinical significance are restricted in their applications. Most methods to date lack either the accuracy to discriminate or the sensitivity to be an efficient technique. As a result, there is an urgent need for a scanning method with the potential to identify precise mutations and with the sensitivity to analyze tumors or germline DNA in pooled samples.